标题： TDACloud：使用拓扑数据分析的点云识别 显示英文标题

标题： TDACloud: Point Cloud Recognition Using Topological Data Analysis

摘要： 基于点云的对象/场所识别在自动驾驶、场景重建和定位等应用中仍然是一个感兴趣的问题。 从查询点云中提取可以与收集到的点云描述符匹配的有意义的局部描述符是一个具有挑战性的问题。 此外，当查询点云存在噪声或已被变换（例如，旋转）时，会增加复杂性。 为此，我们提出了一种名为TDACloud的新方法，使用拓扑数据分析（TDA）从点云中提取局部描述符，该方法不需要资源密集型的基于GPU的机器学习训练。 更具体地说，我们使用ATOL向量化方法为点云生成向量。 与体素化不同，我们提出的技术可以将原始点云作为输入，并输出一个固定大小的TDA描述符向量。 为了测试所提出的TDACloud技术的质量，我们在多个真实世界（例如，Oxford RobotCar，KITTI-360）和现实（例如，ShapeNet）点云数据集上实现了该技术，用于对象和场所识别。 我们还在存在噪声和变换的测试案例上测试了TDACloud，其中查询点云已被缩放、平移或旋转。 我们的结果表明，在噪声条件下和大规模真实世界场所识别中都具有高识别准确率，并且相比基线方法最多提高了约14%。 显示英文摘要

摘要： Point cloud-based object/place recognition remains a problem of interest in applications such as autonomous driving, scene reconstruction, and localization. Extracting meaningful local descriptors from a query point cloud that can be matched with the descriptors of the collected point clouds is a challenging problem. Furthermore, when the query point cloud is noisy or has been transformed (e.g., rotated), it adds to the complexity. To this end, we propose a novel methodology, named TDACloud, using Topological Data Analysis (TDA) for local descriptor extraction from a point cloud, which does not need resource-intensive GPU-based machine learning training. More specifically, we used the ATOL vectorization method to generate vectors for point clouds. Unlike voxelization, our proposed technique can take raw point clouds as inputs and outputs a fixed-size TDA-descriptor vector. To test the quality of the proposed TDACloud technique, we have implemented it on multiple real-world (e.g., Oxford RobotCar, KITTI-360) and realistic (e.g., ShapeNet) point cloud datasets for object and place recognition. We have also tested TDACloud on noisy and transformed test cases where the query point cloud has been scaled, translated, or rotated. Our results demonstrate high recognition accuracies in noisy conditions and large-scale real-world place recognition while outperforming the baselines by up to approximately 14%.